近幾年ETF在台灣迅速地發展，隨著衍生性商品的多樣化，使得預測商品價格的波動變得格外重要且也日漸受到投資人的重視，因此本文欲透過RW、GARCH、EGARCH、GJR-GARCH做為預測波動模型去捕捉6檔兩岸成分ETF的波動性，再透過二種損失函數(loss function)與DM檢定去區分模型間的預測能力並進行優劣排序，最後找出相對最佳預測模型以提供市場參與者作為投資交易策略之重要參考依據。
利用GARCH模型來描述ETF波動叢聚、厚尾等特性，其次，利用EGARCH和GJR-GARCH模型來捕捉波動具有不對稱的特性，最終將模型進行配適及預測。實證發現最佳模型的決定對於不同損失函數的選取是敏感的，相對來說EGARCH模型對於台灣ETF價格有較好的預測能力，顯示ETF價格波動呈非對稱性且具槓桿效果，即壞消息比好消息更容易引起市場較大幅度的波動，而中國ETF價格較無法藉由單一模型去描述及預測。此外，分別以每日真實波動性和PK變幅日波動性當做真實波動之代理變數，結果發現無法透過PK變幅日波動性去觀察其預測績效，因此，本文認為預測兩岸成分ETF價格波動性時，採用每日真實波動性當做波動代理變數會比PK變幅日波動性更為理想。

In the recent years, ETFs developed in Taiwan rapidly. With the diversities of the Financial Derivatives, it is virtually important that forecasting the volatility of merchandise price, and it also be valued by investers. As a result, this paper want to use RW, GARCH, EGARCH, and GJR-GARCH for forecasting volatility model to catch the volatilities of six ETFs composing by Taiwan and China. Forthermore, using two kinds of loss functions and DM test to distinguish forecasting abilities between these models, and progress order of merit. Finally, finding the best forecasting model can give market participants important reference for investment trading strategy.
The first, using GARCH model demonstrates characteristics of ETFs about volatility clustering and fat-tail.The next, using EGARCH and GJR-GARCH models catches volatilities with asymmetric characteristics. In the third place, find this paper fits and forecasts from these models. Empirical demonstrates that decision of the best model is sensitive for choosing different loss functions. Relatively speaking, EGARCH model has a much better forecasting ability for ETF price in Taiwan and demonstrates that pricing volatilities of ETF have asymmetric and levering effect. In other words, bad news can easily cause greater volatilities than good news in financial markets. But the price of ETF in China can not use the only model to demonstrate and forecast. In addition, this paper views real daily volatility and PK-range as the proxy of real volatilities. The results find that using PK-range is impossible to observe forecasting performance. As a result, this paper believe that using daily real volatility as volatility proxy is more ideal than PK-range when forecasting the pricing volatilities of ETF composing by Taiwan and China.

目　錄
謝　辭	I
目　錄	IV
表　目　錄	VI
圖　目　錄	VIII
第一章　緒論	1
第一節  研究背景與動機	1
第二節  研究目的	4
第三節  研究架構與流程	5
第二章　理論與相關文獻	6
第一節　ETF與波動性的特色	6
第二節  一籃子交易對波動性的影響	11
第三節  預測模型相關文獻	13
第三章 研究方法	18
第一節  研究資料定義與來源	18
第二節  模型定義與概念	20
第三節  波動預測之績效評估準則	24
第四節  實證步驟	26
第四章　實證結果與分析	27
第一節　敘述統計量分析	27
第二節　模型配適及參數估計	32
第三節　預測績效評估	43
第五章　結論與建議	51
第一節　結論	51
第二節　建議	52
參考文獻	54
表　目　錄
表 1  ETF與共同基金簡易比較表	8
表 2  各ETF的研究期間	18
表 3  ETF股價之敘述統計量分析	29
表 4  ETF報酬率之敘述統計量分析	30
表 5  台灣50之模型配適	33
表 6  台灣中型100之模型配適	34
表 7  高股息之模型配適	35
表 8  寶滬深之模型配適	36
表 9  FB上証之模型配適	37
表 10  上證50之模型配適	38
表 11  台灣50之模型參數估計	39
表 12  台灣中型100之模型參數估計	39
表 13  高股息之模型參數估計	39
表 14  寶滬深之模型參數估計	40
表 15  FB上証之模型參數估計	40
表 16  上證50之模型參數估計	40
表 17  台灣50之損失函數	44
表 18  台灣中型100之損失函數	45
表 19  高股息之損失函數	45
表 20  寶滬深之損失函數	46
表 21  FB上証之損失函數	46
表 22  上證50之損失函數	47
表 23  樣本外預測之DM檢定(每日真實波動性)	49
表 24  樣本外預測之DM檢定(PK變幅日波動性)	50
表 25  預測績效之DM檢定	50
圖　目　錄
圖 1  全球ETFs/ETPs近十六年來的成長	1
圖 2  研究流程圖	5
圖 3  申購與買回程序及ETF價格與淨值之關係圖	7
圖 4  各檔ETF價格與報酬率之時間序列	28
圖 5  各檔ETF報酬率之相關統計分配	31
圖 6  台灣50價格波動之預測走勢圖	41
圖 7  台灣中型100價格波動之預測走勢圖	41
圖 8  高股息價格波動之預測走勢圖	41
圖 9  寶滬深價格波動之預測走勢圖	42
圖 10  FB上証價格波動之預測走勢圖	42
圖 11  上證50價格波動之預測走勢圖	42

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